Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala:: Method and validation on controls and patients with Alzheimer's disease
Anatomically constrained region deformation for the automated segmentation of the hippocampus and the amygdala:: Method and validation on controls and patients with Alzheimer's disease
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DOI：
10.1016/j.neuroimage.2006.10.035
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发表时间：
2007-02-01
期刊：
影响因子：
5.7
通讯作者：
Garnero, Line
中科院分区：
文献类型：
--
作者：
Chupin, Marie;Mukuna-Bantumbakulu, A. Romain;Garnero, Line
我们描述了一种新的算法的海马（He）和杏仁核（Am）在临床磁共振成像（MRI）扫描的自动分割。基于同伦变形区域，我们的迭代方法允许同时提取两种结构，通过双竞争增长。我们的方法的最原始的功能之一是变形约束的基础上的先验知识的解剖特征，自动检索的MRI数据。唯一的手动干预包括边界框的定义和两个种子的定位;两个结构的总执行时间在5到7分钟之间，包括初始化。对16名年轻健康受试者和8名阿尔茨海默病（AD）患者的萎缩范围从有限到严重的方法进行了评估。性能的三个方面的特点是验证方法：准确性（自动与手动分割），自动分割的再现性和手动分割的再现性。对于16名年轻健康受试者，准确度的特征在于平均相对体积误差/重叠/最大边界距离，He为7%/84%/4.5 mm，Am为12%/81%/3.9 mm;对于8名阿尔茨海默病患者，He为9%/ 84%/6.5 mm，Am为15%/76%/4.5 mm。我们的结论是，这种新方法的性能，从健康和患病的受试者在分割质量，再现性和时间效率方面的数据与以前出版的手动和自动分割方法相比，毫不逊色。所提出的方法提供了一个新的框架，进一步发展的定量分析病理海马和杏仁核的MRI扫描。(c)2006年爱思唯尔公司All rights reserved.
We describe a new algorithm for the automated segmentation of the hippocampus (He) and the amygdala (Am) in clinical Magnetic Resonance Imaging (MRI) scans. Based on homotopically deforming regions, our iterative approach allows the simultaneous extraction of both structures, by means of dual competitive growth. One of the most original features of our approach is the deformation constraint based on prior knowledge of anatomical features that are automatically retrieved from the MRI data. The only manual intervention consists of the definition of a bounding box and positioning of two seeds; total execution time for the two structures is between 5 and 7 min including initialisation. The method is evaluated on 16 young healthy subjects and 8 patients with Alzheimer's disease (AD) for whom the atrophy ranged from limited to severe. Three aspects of the performances are characterised for validating the method: accuracy (automated vs. manual segmentations), reproducibility of the automated segmentation and reproducibility of the manual segmentation. For 16 young healthy subjects, accuracy is characterised by mean relative volume error/overlap/maximal boundary distance of 7%/84%/4.5 mm for He and 12%/81%/3.9 mm for Am; for 8 Alzheimer's disease patients, it is 9%/ 84%/6.5 mm for He and 15%/76%/4.5 mm for Am. We conclude that the performance of this new approach in data from healthy and diseased subjects in terms of segmentation quality, reproducibility and time efficiency compares favourably with that of previously published manual and automated segmentation methods. The proposed approach provides a new framework for further developments in quantitative analyses of the pathological hippocampus and amygdala in MRI scans. (c) 2006 Elsevier Inc. All rights reserved.